| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
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| Presentation Title |
Advanced Mechanical Properties Prediction of Functionally Graded Materials through High-Throughput Characterization. |
| Author(s) |
C. Bean, Y. Nie, M.A. Charpagne, J.C. Stinville |
| On-Site Speaker (Planned) |
J.C. Stinville |
| Abstract Scope |
Advances in high-throughput material characterization, complemented by computational and machine-learning techniques, have ostensibly brought the longstanding goal of accelerating material discovery within grasp. With these advancements, materials of interest with favorable composition can be identified using computed approaches or combinatorial synthesis. However, mechanical characterization considerably hampers this materials development cycle. High-fidelity macroscopic testing is time-consuming and remains the sole method for obtaining advanced mechanical properties. The present study presents an accelerated route for evaluating advanced mechanical properties by leveraging inverse analysis of large datasets of nanometer-scale plastic localization collected through high-resolution imaging automation and analyzed by computer vision. We identified correlations between the characteristics of the plastic localization events and macroscopic properties. These correlations enable a swift evaluation of advanced mechanical properties. It is demonstrated in assessing the temperature-dependent fatigue strength within a single test. Moreover, this study presents a means of rapidly predicting microstructure effects of functionally graded materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, Characterization, Additive Manufacturing |